A second reason for concern is the distribution of impacts among people and
across regions. The impacts of climate change will not be distributed equally.
Some individuals, sectors, systems, and regions will be less affectedor
may even benefit; other individuals, sectors, systems, and regions may suffer
significant losses. This pattern of relative benefits or losses is not likely
to remain constant over time. It will be different with different magnitudes
of climate change. Some regions may have gains only for certain changes in temperature
and precipitation and not for others. As a result, some regions that may first
see net benefits eventually may face losses as well as the climate continues
to warm.

19.4.1. Analysis of Distributional Incidence: State of
the Art

Research into the distribution of impacts of climate change is in its infancy,
in large measure because this research poses several methodological challenges.

A first difficulty is synthesisthe need to reduce the complex pattern
of individual impacts to a more tractable set of regional or sectoral indicators.
The challenge is to identify a set of indicators that can summarize and make
comparable the impacts in different regions, sectors, or systems in a meaningful
way. A range of indicators and methods have been put forward. Many models use
physical measures such as the number of people affected (e.g., Hoozemans et
al., 1993), change in net primary productivity (NPP) (White et al., 1999), or
the number of systems undergoing change (e.g., Alcamo et al., 1995).

The most widespread numeraire, however, is economic cost (Nordhaus, 1991, 1994a;
Cline, 1992; Hohmeyer and Gaertner, 1992; Titus, 1992; Downing et al., 1995,
1996; Fankhauser, 1995; Tol, 1995; Mendelsohn and Neumann, 1999). This numeraire
is particularly well-suited to measure market impactsthat is, impacts
that are linked to market transactions and directly affect GDP (i.e., a country's
national accounts). The costs of sea-level rise, for example, can be expressed
as the capital cost of protection plus the economic value of land and structures
at loss or at risk; agricultural impacts can be expressed as costs or benefits
to producers and consumers, including the incremental costs of adaptation. Using
a monetary numeraire to express nonmarket impacts such as effects on ecosystems
or human health is more difficult. It is possible in principle, however. There
is a broad and established literature on valuation theory and its application,
including studies (mostly in a nonclimate change context) on the monetary value
of lower mortality risk, ecosystems, quality of life, and so forth. However,
economic valuation can be controversial and requires sophisticated analysis,
which still is mostly lacking in a climate change context.

Physical metricssuch as NPP or percentage of systems affectedon
the other hand, are best suited for natural systems. When they are applied to
systems under human management, they suffer from being poorly linked to human
welfare, the ultimate indicator of concern. Some researchers therefore recommend
different numeraires for market impacts, mortality, ecosystems, quality of life,
and equity (Schneider et al., 2000b). They recognize, however, that final comparisons
across different numeraires nonetheless are required; they regard this as the
job of policymakers, however.

Persistent knowledge gaps is a second source of difficulty. Distributional
analysis depends heavily on the geographical details of climate change, but
these details are one of the major uncertainties in the outputs of climate change
models. This is particularly true for estimates of precipitation; for example,
estimates of water-sector impact can vary widely depending on the choice of
GCM.3
Uncertainties continue at the level of impact analysis. Despite a growing number
of country-level case studies, our knowledge of local impacts is still too uneven
and incomplete for a careful, detailed comparison across regions. Furthermore,
differences in assumptions often make it difficult to compare case studies across
countries. Only a few studies try to provide a coherent global picture on the
basis of a uniform set of assumptions. The basis of most such global impact
assessments tends to be studies undertaken in developed countriesoften
the United Stateswhich are then extrapolated to other regions. Such extrapolation
is difficult and will be successful only if regional circumstances are carefully
taken into account, including differences in geography, level of development,
value systems, and adaptive capacity. Not all analyses are equally careful in
undertaking this task.

There are other shortcomings that affect the quality of analysis. Although
our understanding of the vulnerability of developed countries is improvingat
least with respect to market impactsinformation about developing countries
is quite limited. Nonmarket damages, indirect effects (e.g., the effect of changed
agricultural output on the food-processing industry), the link between market
and nonmarket effects (e.g., how the loss of ecosystem functions will affect
GDP), and the sociopolitical implications of change also are still poorly understood.
Uncertainty, transient effects (the impact of a changing rather than a changed
and static climate), and the influence of climate variability are other factors
that deserve more attention. Because of these knowledge gaps, distributional
analysis has to rely on (difficult) expert judgment and extrapolation if it
is to provide a comprehensive picture.

Box 19-2. The Impact of Climate Change on Coastal Zones

The impact of sea-level rise has been widely studied for many parts of
the world. Although uncertainties remain, several generic conclusions
can be drawn. First, impacts will not be distributed evenly. Islands and
deltas are particularly vulnerable. Second, forward-looking and sustainable
economic development, coupled with efficient adaptation (mostly protection
of vulnerable shores), can significantly reduce the economic impact of
sea-level rise. Some analysts have even found that coastal vulnerability
may decrease if the rate of economic development is sufficiently high
and climate change sufficiently slow. However, not all countries will
be able to undertake the necessary adaptation investments without outside
financial assistance, and uncertainty about sea levels (e.g., as a result
of storm surges) may make it difficult to identify efficient policies.
Third, coastal wetlands can cope with a relatively modest rate of sea-level
rise, but not with a fast one. Additional wetlands could be lost if their
migration is blocked by hard structures built to protect developed coastal
areas. Fourth, most of the impact will not be through gradual sea-level
rise but through extreme events such as floods and storms. This makes
people without insurance or a strong social network especially vulnerable.
Thus, as a whole, sea-level rise is likely to have strong negative effects
on some people, even if the aggregate impact is limited. Fifth, the aggregate
impact of sea-level rise could be roughly proportional to the observed
rise. At a local scale, however, sea-level rise is more likely to be felt
through successive crossings of thresholds.

A third problem is adaptation. There has been substantial
progress in the treatment of adaptation since the SAR, but adaptation is difficult
to capture adequately in an impact assessment. Adaptation will entail complex
behavioral, technological, and institutional adjustments at all levels of society,
and the capacity to undertake them will vary considerably (see Chapter
18). Various approaches are used to model adaptation (e.g., spatial analogs,
microeconomic modeling), but they are prone to systematic errors about its effectiveness.
The standard approach used in coastal impact assessment and in many agricultural
models is to include in the analysis a limited number of "prominent" but ultimately
arbitrary adaptations. This underestimates adaptive capacity because many potentially
effective adaptations are excluded (Tol et al., 1998). On the other hand, approaches
that are based on analogssuch as the Ricardian approach used by, for example,
Mendelsohn et al. (1994), Mendelsohn and Dinar (1998), and Darwin (1999)probably
overestimate adaptive capacity because they neglect the cost of transition and
learning. This is especially true for cases in which adaptation in developed
countries today is used as a proxy for worldwide adaptation to an uncertain
future climate. Only a very few studies model adaptation as an optimization
process in which agents trade off the costs and benefits of different adaptation
options (Fankhauser, 1995; Yohe et al., 1995, 1996).

The analysis is further complicated by the strong link between adaptation and
other socioeconomic trends. The world will change substantially in the future,
and this will affect vulnerability to climate change. For example, a successful
effort to roll back malaria (as promoted by the development community) could
reduce the negative health effects of climate change. On the other hand, growing
pressure on natural resources from unsustainable economic development is likely
to exacerbate the impacts of climate change on natural systems. Even without
explicit adaptation, impact assessments therefore depend on the "type"
of socioeconomic development expected in the future. The sensitivity of estimates
to such baseline trends can be strong enough in some cases to reverse the sign
(i.e., a potentially negative impact can become positive under a suitable development
path, or vice versa) (Mendelsohn and Neumann, 1999).

Despite the limits in knowledge, a few general patterns emerge with regard
to the distribution of climate change impacts. These patterns are derived from
general principles, observations of past vulnerabilities, and limited modeling
studies.